1-20 of 76
Keywords: machine learning
Close
Follow your search
Access your saved searches in your account

Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200009-MS
... spatiotemporal analysis of microseismic events, with those achieved through seismic moment tensor inversion confirms that the collective behavior analysis gives fair estimates of fracture spatial evolutions. machine learning reservoir characterization intervención de pozos petroleros production...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200019-MS
... Abstract Recently machine learning has being extensively deployed for oil and gas industry for improving result and expedite process. However, the black box models do not explain their prediction which considered as a barrier to adopt machine learning. This paper is about optimizing hydraulic...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-199967-MS
... Abstract This paper builds on Klenner et al. 2018 , which utilized machine learning to understand well-to-well communication ("Frac hits") or fracture-driven interaction (FDI) during hydraulic fracturing operations. This paper introduces an infrastructure that enahances the process for real...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-199988-MS
... analytical approach was developed by Oda (1985) . More advanced flow-based upscaling techniques and local-global upscaling schemes have also been developed ( Chen et al., 2003 ). pressure transient analysis machine learning production monitoring production forecasting optimization problem pressure...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200003-MS
... rate machine learning cnn model wellhead pressure computational resource deep learning convolutional neural network ball seat event u-net model The ball seat event recognition model is an important part of the hydraulic fracturing stage-wise key performance indicator (KPI) auto...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200000-MS
... Abstract An Augmented AI approach has been developed to optimize completion design parameters and access the full potential of unconventional assets by leveraging big data sculpting, domain-induced feature engineering, and robust and explainable machine learning models with quantified...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200006-MS
.... artificial intelligence classification bit selection drilling operation machine learning drilling equipment dysfunction indicator metric information baseline mse value strength drilling parameter drillstring design upstream oil & gas dysfunction identification founder point chart...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200021-MS
... reliable and unique SRV structure that streamlines forward modeling and simulations in unconventional reservoirs as well as contributes to solving inverse problems more mechanistically. fluid modeling reservoir geomechanics well logging machine learning production monitoring reservoir...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189815-MS
... technique translates the gradational variation of multiple subsurface parameters into a continuous map of relative economic value, which can then be used to discuss a multitude of appraisal and development issues. machine learning unconventional resource economics geologic subset Energy Economics...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189786-MS
... uncertainty can also be achieved, which assists in understanding the range of parameters which can be used to successfully match the flowback data. flow in porous media history matching palisade evolver equivalent generation machine learning solver optimization problem Fluid Dynamics iteration...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189806-MS
... Artificial Intelligence neural network residual saturation imbibition saturation spontaneous imbibition fracture machine learning flow in porous media shale gas Upstream Oil & Gas Fluid Dynamics relative permeability water saturation complex reservoir water imbibition Dehghanpour...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189830-MS
... machine learning neural network data preparation fluid modeling equation of state unconventional resource conference hydraulic fracturing classification unsupervised neural network classification model spe csur unconventional resource conference Technology Conference Alberta Schlumberger...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189811-MS
... automatically, semi-automatically using machine learning, or manually, to create a minute-by-minute timeline of rig operations. Operations are then classified both by operation – steering, reaming, making hole, etc. – and well plan to understand how operational demands change automation system utilization. This...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189802-MS
... approaches for horizontal wells in tight/shale reservoirs. production control production monitoring Reservoir Surveillance production forecasting Modeling & Simulation hyperbolic model machine learning certainty reserves evaluation workflow Artificial Intelligence complex reservoir...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189808-MS
... centroid information machine learning Artificial Intelligence data-based smart model loading production rate liquid loading neural network criteria prediction algorithm learning algorithm initial centroid liquid loading problem unconventional gas reservoir turner rate Unconventional...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189790-MS
... study, the approach was executed on Eagle Ford wells. Over 2000 data points were collected with completion sensitivity performed on a multithreaded cluster environment on these wells. Advanced machine learning and data mining algorithms of data analytics such as random forest, gradient boost, linear...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189823-MS
... Abstract This paper presents the use of machine learning via a multiple linear regression and a neural network to solve the complex problem of optimizing completions and well designs in the Duvernay shale. Solutions were revealed that could save over a million dollars per well, along with...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189791-MS
... analysis water saturation Niobrara shale maturity evaluation hydrocarbon shale petroleum reservoir Upstream Oil & Gas porosity pore throat resistivity equation maturation trajectory reservoir machine learning well logging Aguilera thermal maturity determination Pickett plot...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189797-MS
.... shale gas Reservoir Characterization machine learning complex reservoir Artificial Intelligence Upstream Oil & Gas water saturation gas production reservoir pressure shale Burgos Basin condensate porosity Pimienta shale Texas well habano 1 average reservoir pressure saturation...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189809-MS
... classical one, to model the flow and production behavior in highly fractured unconventional reservoirs. complex reservoir normal diffusion fracture network fracture system Artificial Intelligence relation anomalous diffusion diffusion matrix machine learning Upstream Oil & Gas fracture...

Product(s) added to cart

Close Modal